{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T22:50:47Z","timestamp":1792795847549,"version":"4.5.0"},"reference-count":18,"publisher":"Oxford University Press (OUP)","issue":"1","license":[{"start":{"date-parts":[[2019,11,21]],"date-time":"2019-11-21T00:00:00Z","timestamp":1574294400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/academic.oup.com\/journals\/pages\/open_access\/funder_policies\/chorus\/standard_publication_model"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,3,1]]},"abstract":"<jats:title>Summary<\/jats:title>\n               <jats:p>Instrumental variable methods can identify causal effects even when the treatment and outcome are confounded. We study the problem of imperfect measurements of the binary instrumental variable, treatment and outcome. We first consider nondifferential measurement errors, that is, the mismeasured variable does not depend on other variables given its true value. We show that the measurement error of the instrumental variable does not bias the estimate, that the measurement error of the treatment biases the estimate away from zero, and that the measurement error of the outcome biases the estimate toward zero. Moreover, we derive sharp bounds on the causal effects without additional assumptions. These bounds are informative because they exclude zero. We then consider differential measurement errors, and focus on sensitivity analyses in those settings.<\/jats:p>","DOI":"10.1093\/biomet\/asz060","type":"journal-article","created":{"date-parts":[[2019,9,27]],"date-time":"2019-09-27T00:33:19Z","timestamp":1569544399000},"page":"238-245","source":"Crossref","is-referenced-by-count":14,"title":["Measurement errors in the binary instrumental variable model"],"prefix":"10.1093","volume":"107","author":[{"given":"Zhichao","family":"Jiang","sequence":"first","affiliation":[{"name":"Department of Biostatistics and Epidemiology, School of Public Health and Health Sciences, University of Massachusetts, 715 North Pleasant Street, Amherst, Massachusetts 01003, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Statistics, University of California, 425 Evans Hall, Berkeley, California 94720, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2019,11,21]]},"reference":[{"key":"2020021310531543400_B1","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1080\/01621459.1995.10476535","article-title":"Two-stage least squares estimation of average causal effects in models with variable treatment intensity","volume":"90","author":"Angrist,","year":"1995","journal-title":"J. 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